Xavier: a robot navigation architecture based on partially observable Markov decision process models
Artificial intelligence and mobile robots
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Texture Classification by Subsets of Local Binary Patterns
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
From omnidirectional images to hierarchical localization
Robotics and Autonomous Systems
Omnidirectional Vision Based Topological Navigation
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
COLD: The CoSy Localization Database
International Journal of Robotics Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically inspired mobile robot vision localization
IEEE Transactions on Robotics
SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments
Robotics and Autonomous Systems
Hierarchical appearance-based classifiers for qualitative spatial localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
International Journal of Robotics Research
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
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In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.